Overview

Dataset statistics

Number of variables13
Number of observations3961
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory402.4 KiB
Average record size in memory104.0 B

Variable types

NUM13

Warnings

df_index has unique values Unique

Reproduction

Analysis started2020-11-11 20:10:16.389716
Analysis finished2020-11-11 20:11:15.652659
Duration59.26 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct3961
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2414.467054
Minimum0
Maximum4897
Zeros1
Zeros (%)< 0.1%
Memory size30.9 KiB
2020-11-11T12:11:15.871592image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile237
Q11170
median2385
Q33641
95-th percentile4661
Maximum4897
Range4897
Interquartile range (IQR)2471

Descriptive statistics

Standard deviation1426.02502
Coefficient of variation (CV)0.5906168892
Kurtosis-1.207715853
Mean2414.467054
Median Absolute Deviation (MAD)1235
Skewness0.04938645192
Sum9563704
Variance2033547.359
MonotocityStrictly increasing
2020-11-11T12:11:16.081030image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
20471< 0.1%
 
26001< 0.1%
 
25961< 0.1%
 
46431< 0.1%
 
5451< 0.1%
 
25921< 0.1%
 
46391< 0.1%
 
5411< 0.1%
 
46351< 0.1%
 
46311< 0.1%
 
Other values (3951)395199.7%
 
ValueCountFrequency (%) 
01< 0.1%
 
11< 0.1%
 
21< 0.1%
 
31< 0.1%
 
61< 0.1%
 
ValueCountFrequency (%) 
48971< 0.1%
 
48961< 0.1%
 
48951< 0.1%
 
48941< 0.1%
 
48931< 0.1%
 

fa
Real number (ℝ≥0)

Distinct68
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.839346125
Minimum3.8
Maximum14.2
Zeros0
Zeros (%)0.0%
Memory size30.9 KiB
2020-11-11T12:11:16.379232image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum3.8
5-th percentile5.6
Q16.3
median6.8
Q37.3
95-th percentile8.3
Maximum14.2
Range10.4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8668597405
Coefficient of variation (CV)0.1267459966
Kurtosis2.253047398
Mean6.839346125
Median Absolute Deviation (MAD)0.5
Skewness0.6961002189
Sum27090.65
Variance0.7514458097
MonotocityNot monotonic
2020-11-11T12:11:16.615602image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
6.82416.1%
 
6.62386.0%
 
6.42245.7%
 
6.91914.8%
 
6.71904.8%
 
6.51824.6%
 
71794.5%
 
6.21594.0%
 
6.31584.0%
 
7.11543.9%
 
Other values (58)204551.6%
 
ValueCountFrequency (%) 
3.81< 0.1%
 
3.91< 0.1%
 
4.220.1%
 
4.430.1%
 
4.51< 0.1%
 
ValueCountFrequency (%) 
14.21< 0.1%
 
11.81< 0.1%
 
10.71< 0.1%
 
10.320.1%
 
10.21< 0.1%
 

va
Real number (ℝ≥0)

Distinct125
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.280537743
Minimum0.08
Maximum1.1
Zeros0
Zeros (%)0.0%
Memory size30.9 KiB
2020-11-11T12:11:16.995779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.08
5-th percentile0.15
Q10.21
median0.26
Q30.33
95-th percentile0.46
Maximum1.1
Range1.02
Interquartile range (IQR)0.12

Descriptive statistics

Standard deviation0.103437087
Coefficient of variation (CV)0.3687100562
Kurtosis5.327754
Mean0.280537743
Median Absolute Deviation (MAD)0.06
Skewness1.641080979
Sum1111.21
Variance0.01069923096
MonotocityNot monotonic
2020-11-11T12:11:17.498431image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.282135.4%
 
0.242085.3%
 
0.262075.2%
 
0.251794.5%
 
0.221784.5%
 
0.21754.4%
 
0.271754.4%
 
0.231734.4%
 
0.211584.0%
 
0.31543.9%
 
Other values (115)214154.1%
 
ValueCountFrequency (%) 
0.0820.1%
 
0.0851< 0.1%
 
0.091< 0.1%
 
0.160.2%
 
0.10540.1%
 
ValueCountFrequency (%) 
1.11< 0.1%
 
1.0051< 0.1%
 
0.9651< 0.1%
 
0.931< 0.1%
 
0.911< 0.1%
 

ca
Real number (ℝ≥0)

Distinct87
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3343322393
Minimum0
Maximum1.66
Zeros18
Zeros (%)0.5%
Memory size30.9 KiB
2020-11-11T12:11:17.891409image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.17
Q10.27
median0.32
Q30.39
95-th percentile0.53
Maximum1.66
Range1.66
Interquartile range (IQR)0.12

Descriptive statistics

Standard deviation0.1224460908
Coefficient of variation (CV)0.3662407521
Kurtosis6.84480817
Mean0.3343322393
Median Absolute Deviation (MAD)0.06
Skewness1.310601017
Sum1324.29
Variance0.01499304514
MonotocityNot monotonic
2020-11-11T12:11:18.294852image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.32396.0%
 
0.282205.6%
 
0.322145.4%
 
0.341814.6%
 
0.291794.5%
 
0.261734.4%
 
0.491734.4%
 
0.271644.1%
 
0.311624.1%
 
0.331553.9%
 
Other values (77)210153.0%
 
ValueCountFrequency (%) 
0180.5%
 
0.0160.2%
 
0.0260.2%
 
0.0320.1%
 
0.04100.3%
 
ValueCountFrequency (%) 
1.661< 0.1%
 
1.231< 0.1%
 
150.1%
 
0.991< 0.1%
 
0.911< 0.1%
 

rs
Real number (ℝ≥0)

Distinct310
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.91481949
Minimum0.6
Maximum65.8
Zeros0
Zeros (%)0.0%
Memory size30.9 KiB
2020-11-11T12:11:18.620040image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile1.1
Q11.6
median4.7
Q38.9
95-th percentile15.2
Maximum65.8
Range65.2
Interquartile range (IQR)7.3

Descriptive statistics

Standard deviation4.861646308
Coefficient of variation (CV)0.8219433097
Kurtosis5.681512166
Mean5.91481949
Median Absolute Deviation (MAD)3.2
Skewness1.333639018
Sum23428.6
Variance23.63560482
MonotocityNot monotonic
2020-11-11T12:11:18.812037image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.41654.2%
 
1.21654.2%
 
1.61443.6%
 
1.31343.4%
 
1.11263.2%
 
1.51253.2%
 
1.7872.2%
 
1.8852.1%
 
1771.9%
 
2671.7%
 
Other values (300)278670.3%
 
ValueCountFrequency (%) 
0.61< 0.1%
 
0.770.2%
 
0.8250.6%
 
0.9350.9%
 
0.9530.1%
 
ValueCountFrequency (%) 
65.81< 0.1%
 
31.61< 0.1%
 
26.051< 0.1%
 
23.51< 0.1%
 
22.61< 0.1%
 

chlorides
Real number (ℝ≥0)

Distinct160
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04590507448
Minimum0.009
Maximum0.346
Zeros0
Zeros (%)0.0%
Memory size30.9 KiB
2020-11-11T12:11:19.017445image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.009
5-th percentile0.027
Q10.035
median0.042
Q30.05
95-th percentile0.069
Maximum0.346
Range0.337
Interquartile range (IQR)0.015

Descriptive statistics

Standard deviation0.0231027148
Coefficient of variation (CV)0.5032714807
Kurtosis35.53028798
Mean0.04590507448
Median Absolute Deviation (MAD)0.007
Skewness4.969076318
Sum181.83
Variance0.0005337354313
MonotocityNot monotonic
2020-11-11T12:11:19.198958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.0361654.2%
 
0.0421553.9%
 
0.0441553.9%
 
0.0461523.8%
 
0.041523.8%
 
0.0471453.7%
 
0.0381403.5%
 
0.0341373.5%
 
0.0371363.4%
 
0.0481353.4%
 
Other values (150)248962.8%
 
ValueCountFrequency (%) 
0.0091< 0.1%
 
0.0121< 0.1%
 
0.0131< 0.1%
 
0.01440.1%
 
0.01530.1%
 
ValueCountFrequency (%) 
0.3461< 0.1%
 
0.3011< 0.1%
 
0.291< 0.1%
 
0.2711< 0.1%
 
0.2551< 0.1%
 

fsd
Real number (ℝ≥0)

Distinct132
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.8891694
Minimum2
Maximum289
Zeros0
Zeros (%)0.0%
Memory size30.9 KiB
2020-11-11T12:11:19.464283image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile11
Q123
median33
Q345
95-th percentile63
Maximum289
Range287
Interquartile range (IQR)22

Descriptive statistics

Standard deviation17.21002061
Coefficient of variation (CV)0.4932768795
Kurtosis13.43402487
Mean34.8891694
Median Absolute Deviation (MAD)11
Skewness1.56668022
Sum138196
Variance296.1848094
MonotocityNot monotonic
2020-11-11T12:11:19.685690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
291253.2%
 
311102.8%
 
341072.7%
 
261052.7%
 
361022.6%
 
241012.5%
 
35972.4%
 
28952.4%
 
23932.3%
 
25922.3%
 
Other values (122)293474.1%
 
ValueCountFrequency (%) 
21< 0.1%
 
390.2%
 
490.2%
 
5230.6%
 
6290.7%
 
ValueCountFrequency (%) 
2891< 0.1%
 
146.51< 0.1%
 
138.51< 0.1%
 
1311< 0.1%
 
1281< 0.1%
 

tsd
Real number (ℝ≥0)

Distinct251
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean137.1935117
Minimum9
Maximum440
Zeros0
Zeros (%)0.0%
Memory size30.9 KiB
2020-11-11T12:11:19.923092image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile73
Q1106
median133
Q3166
95-th percentile212
Maximum440
Range431
Interquartile range (IQR)60

Descriptive statistics

Standard deviation43.12906524
Coefficient of variation (CV)0.314366654
Kurtosis0.7352578602
Mean137.1935117
Median Absolute Deviation (MAD)29
Skewness0.4567996771
Sum543423.5
Variance1860.116268
MonotocityNot monotonic
2020-11-11T12:11:20.122471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
111511.3%
 
114471.2%
 
113461.2%
 
122451.1%
 
128451.1%
 
117441.1%
 
150431.1%
 
126421.1%
 
98411.0%
 
118411.0%
 
Other values (241)351688.8%
 
ValueCountFrequency (%) 
91< 0.1%
 
101< 0.1%
 
181< 0.1%
 
191< 0.1%
 
211< 0.1%
 
ValueCountFrequency (%) 
4401< 0.1%
 
366.51< 0.1%
 
3441< 0.1%
 
3131< 0.1%
 
307.51< 0.1%
 

density
Real number (ℝ≥0)

Distinct890
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9937895304
Minimum0.98711
Maximum1.03898
Zeros0
Zeros (%)0.0%
Memory size30.9 KiB
2020-11-11T12:11:20.331911image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.98711
5-th percentile0.98961
Q10.99162
median0.9935
Q30.99571
95-th percentile0.9986
Maximum1.03898
Range0.05187
Interquartile range (IQR)0.00409

Descriptive statistics

Standard deviation0.002904595778
Coefficient of variation (CV)0.002922747412
Kurtosis14.18489211
Mean0.9937895304
Median Absolute Deviation (MAD)0.00206
Skewness1.273317861
Sum3936.40033
Variance8.436676636e-06
MonotocityNot monotonic
2020-11-11T12:11:20.591217image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.992601.5%
 
0.9928521.3%
 
0.9932491.2%
 
0.9934461.2%
 
0.993461.2%
 
0.9938431.1%
 
0.9944411.0%
 
0.9927401.0%
 
0.9924391.0%
 
0.9954370.9%
 
Other values (880)350888.6%
 
ValueCountFrequency (%) 
0.987111< 0.1%
 
0.987131< 0.1%
 
0.987221< 0.1%
 
0.98741< 0.1%
 
0.9874220.1%
 
ValueCountFrequency (%) 
1.038981< 0.1%
 
1.01031< 0.1%
 
1.002951< 0.1%
 
1.002411< 0.1%
 
1.00241< 0.1%
 

pH
Real number (ℝ≥0)

Distinct103
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.195458218
Minimum2.72
Maximum3.82
Zeros0
Zeros (%)0.0%
Memory size30.9 KiB
2020-11-11T12:11:20.919760image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2.72
5-th percentile2.96
Q13.09
median3.18
Q33.29
95-th percentile3.46
Maximum3.82
Range1.1
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.1515455672
Coefficient of variation (CV)0.0474253008
Kurtosis0.5499570349
Mean3.195458218
Median Absolute Deviation (MAD)0.1
Skewness0.455456831
Sum12657.21
Variance0.02296605892
MonotocityNot monotonic
2020-11-11T12:11:21.131700image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3.161283.2%
 
3.141273.2%
 
3.221193.0%
 
3.191152.9%
 
3.151142.9%
 
3.181142.9%
 
3.241142.9%
 
3.121112.8%
 
3.21112.8%
 
3.11092.8%
 
Other values (93)279970.7%
 
ValueCountFrequency (%) 
2.721< 0.1%
 
2.741< 0.1%
 
2.771< 0.1%
 
2.7920.1%
 
2.830.1%
 
ValueCountFrequency (%) 
3.821< 0.1%
 
3.811< 0.1%
 
3.820.1%
 
3.791< 0.1%
 
3.7720.1%
 

sulphates
Real number (ℝ≥0)

Distinct79
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4903509215
Minimum0.22
Maximum1.08
Zeros0
Zeros (%)0.0%
Memory size30.9 KiB
2020-11-11T12:11:21.352119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.22
5-th percentile0.34
Q10.41
median0.48
Q30.55
95-th percentile0.7
Maximum1.08
Range0.86
Interquartile range (IQR)0.14

Descriptive statistics

Standard deviation0.1135228053
Coefficient of variation (CV)0.2315133924
Kurtosis1.565020602
Mean0.4903509215
Median Absolute Deviation (MAD)0.07
Skewness0.9378533357
Sum1942.28
Variance0.01288742733
MonotocityNot monotonic
2020-11-11T12:11:21.686218image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.51914.8%
 
0.461824.6%
 
0.441714.3%
 
0.381654.2%
 
0.451483.7%
 
0.471443.6%
 
0.421443.6%
 
0.481423.6%
 
0.541363.4%
 
0.41343.4%
 
Other values (69)240460.7%
 
ValueCountFrequency (%) 
0.221< 0.1%
 
0.231< 0.1%
 
0.2540.1%
 
0.2630.1%
 
0.27100.3%
 
ValueCountFrequency (%) 
1.081< 0.1%
 
1.061< 0.1%
 
1.011< 0.1%
 
11< 0.1%
 
0.991< 0.1%
 

alcohol
Real number (ℝ≥0)

Distinct103
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.58935791
Minimum8
Maximum14.2
Zeros0
Zeros (%)0.0%
Memory size30.9 KiB
2020-11-11T12:11:21.917108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile8.9
Q19.5
median10.4
Q311.4
95-th percentile12.8
Maximum14.2
Range6.2
Interquartile range (IQR)1.9

Descriptive statistics

Standard deviation1.217076311
Coefficient of variation (CV)0.1149339103
Kurtosis-0.695979718
Mean10.58935791
Median Absolute Deviation (MAD)0.9
Skewness0.450696598
Sum41944.44667
Variance1.481274748
MonotocityNot monotonic
2020-11-11T12:11:22.099656image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
9.51774.5%
 
9.41694.3%
 
101433.6%
 
9.21403.5%
 
10.51403.5%
 
111303.3%
 
10.41303.3%
 
10.81193.0%
 
91162.9%
 
10.21162.9%
 
Other values (93)258165.2%
 
ValueCountFrequency (%) 
820.1%
 
8.420.1%
 
8.590.2%
 
8.6160.4%
 
8.7461.2%
 
ValueCountFrequency (%) 
14.21< 0.1%
 
14.051< 0.1%
 
1450.1%
 
13.930.1%
 
13.820.1%
 

quality
Real number (ℝ≥0)

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.854834638
Minimum3
Maximum9
Zeros0
Zeros (%)0.0%
Memory size30.9 KiB
2020-11-11T12:11:22.295098image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q15
median6
Q36
95-th percentile7
Maximum9
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8906826795
Coefficient of variation (CV)0.152127726
Kurtosis0.2993451703
Mean5.854834638
Median Absolute Deviation (MAD)1
Skewness0.1120040345
Sum23191
Variance0.7933156355
MonotocityNot monotonic
2020-11-11T12:11:22.429216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
6178845.1%
 
5117529.7%
 
768917.4%
 
41533.9%
 
81313.3%
 
3200.5%
 
950.1%
 
ValueCountFrequency (%) 
3200.5%
 
41533.9%
 
5117529.7%
 
6178845.1%
 
768917.4%
 
ValueCountFrequency (%) 
950.1%
 
81313.3%
 
768917.4%
 
6178845.1%
 
5117529.7%
 

Interactions

2020-11-11T12:10:24.849440image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:25.102807image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:25.329185image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:25.539621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:25.921623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:26.277699image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:26.668684image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:27.057669image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:27.900497image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:28.460542image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:28.990666image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:29.336257image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:29.662894image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:29.966595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:30.286740image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:31.002911image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:31.473688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:31.829756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:32.159754image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:32.528610image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:32.995414image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:33.494328image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:33.809100image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:34.112293image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:34.406505image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:34.614945image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:34.870266image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:35.089059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:35.271573image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:35.438993image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:35.672901image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:35.888355image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:36.078810image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:36.257368image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:36.427394image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:36.598453image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:36.781967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:36.952547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:37.130885image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:37.330320image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:37.515826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:37.742216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:37.949660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:38.136208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:38.338668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:38.549130image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:38.754019image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:38.944272image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:39.131770image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:39.325256image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:39.519767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:39.725218image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:39.922656image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:40.100180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:40.295849image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:40.483901image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:40.668370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:40.848886image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:41.041370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:41.285732image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:42.214062image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:42.595043image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:42.930263image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:43.864761image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:45.052925image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:45.706079image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:46.638940image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:47.353763image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:47.876337image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:48.649785image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:48.990510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:49.424102image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:49.969645image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:50.294375image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:50.605772image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:50.906955image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:51.153296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:51.377730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:51.574239image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:51.762255image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:51.939744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:52.164143image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:52.404501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:52.580611image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:52.766732image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:52.950207image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:53.118759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:53.309983image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:53.488020image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:53.655573image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:53.831137image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:54.020594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:54.185158image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:54.349333image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:54.501482image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:54.673066image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:54.839625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:55.023922image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:55.219099image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:55.394462image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:55.562557image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:55.753046image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:55.921595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:56.112086image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:56.324519image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:56.646166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:56.916952image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:57.095275image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:57.287725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:57.475224image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:57.689652image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:57.952944image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:58.122533image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:58.300059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:58.477784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:58.650046image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:58.829244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:59.024720image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:59.240146image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:10:59.664018image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:00.284629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:00.896574image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:01.413054image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:01.688850image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:02.029935image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:02.529644image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:03.135098image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:03.406372image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:03.725148image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:04.009385image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:04.273678image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:04.446259image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:04.626815image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:04.792346image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:04.987418image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:05.162984image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:05.354434image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:05.527972image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:05.718461image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:06.072036image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:06.569602image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:06.750702image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:06.933212image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:07.110906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:07.298392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:07.525783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:07.751194image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:07.949664image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:08.137108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:08.334191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:08.524077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:08.803485image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:09.021417image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:09.210943image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:09.387439image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:09.905453image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:10.788988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:11.303350image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:11.749671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:12.148607image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:12.431540image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:12.636060image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:12.884699image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:13.248720image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:13.493645image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:13.726704image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:13.955574image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:14.237817image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:14.518068image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-11-11T12:11:22.644640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-11-11T12:11:23.032341image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-11-11T12:11:23.377417image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-11-11T12:11:23.753161image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-11-11T12:11:14.927011image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-11T12:11:15.395721image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

df_indexfavacarschloridesfsdtsddensitypHsulphatesalcoholquality
007.00.270.3620.700.04545.0170.01.00103.000.458.86
116.30.300.341.600.04914.0132.00.99403.300.499.56
228.10.280.406.900.05030.097.00.99513.260.4410.16
337.20.230.328.500.05847.0186.00.99563.190.409.96
466.20.320.167.000.04530.0136.00.99493.180.479.66
598.10.220.431.500.04428.0129.00.99383.220.4511.06
6108.10.270.411.450.03311.063.00.99082.990.5612.05
7118.60.230.404.200.03517.0109.00.99473.140.539.75
8127.90.180.371.200.04016.075.00.99203.180.6310.85
9136.60.160.401.500.04448.0143.00.99123.540.5212.47

Last rows

df_indexfavacarschloridesfsdtsddensitypHsulphatesalcoholquality
395148886.80.2200.361.200.05238.0127.00.993303.040.549.25
395248894.90.2350.2711.750.03034.0118.00.995403.070.509.46
395348906.10.3400.292.200.03625.0100.00.989383.060.4411.86
395448915.70.2100.320.900.03838.0121.00.990743.240.4610.66
395548926.50.2300.381.300.03229.0112.00.992983.290.549.75
395648936.20.2100.291.600.03924.092.00.991143.270.5011.26
395748946.60.3200.368.000.04757.0168.00.994903.150.469.65
395848956.50.2400.191.200.04130.0111.00.992542.990.469.46
395948965.50.2900.301.100.02220.0110.00.988693.340.3812.87
396048976.00.2100.380.800.02022.098.00.989413.260.3211.86